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Data Lake vs Data Warehouse: What's The Key Difference?

Global Data 365
December 21, 2023

With the rise of big data and the explosion of new data sources, traditional data warehousing approaches may not be sufficient to meet the needs of modern data management and analytics, creating confusions between Data Lake vs Data warehouse. This has led to the development of new approaches, including Data Lake and Data Warehouse. Each approach offers unique benefits and drawbacks, and understanding the differences between them is critical to making informed decisions about data management and analytics.

Data Lake

A Data Lake is a centralized repository that allows businesses to store vast amounts of raw, unstructured, or structured data at scale. It provides a flexible storage environment, enabling organizations to ingest diverse data types without the need for upfront structuring. This unrefined data can then be processed and analyzed for valuable insights, making Data Lakes ideal for handling large volumes of real-time and varied data.

Benefits and Use Cases of Data Lake

Data lakes provide scalable and cost-effective storage, accommodating diverse data types such as raw and unstructured data for flexible analysis. With a focus on real-time analytics and advanced capabilities like machine learning, they support innovation in algorithm development. Cost-efficient storage solutions, often leveraging scalable cloud storage, make data lakes economical for managing large datasets.

Use cases range from big data analytics, IoT data management, and ad hoc analysis to long-term data archiving and achieving a 360-degree customer view. In essence, data lakes offer dynamic repositories that empower organizations with flexibility, real-time insights, and comprehensive data management solutions.

Data Warehouse

On the other hand, a Data Warehouse is a structured, organized database optimized for analysis and reporting. It is designed to store structured data from various sources in a format that is easily query able and supports business intelligence reporting. Data Warehouses are characterized by their schema-on-write approach, requiring data to be structured before entering the system, ensuring a high level of consistency for analytical purposes.

Benefits and Use Cases of Data Warehouse

Data warehouses offer a multitude of benefits, including optimized structured data analysis for improved query performance and efficient reporting. They preserve historical data for time-series analysis and audit trails, enhance business intelligence through data consolidation and dashboard creation, ensure data quality and consistency through cleansing processes, and provide scalability to handle growing data volumes.

Common use cases encompass business performance analysis, customer relationship management, supply chain optimization, financial reporting and compliance, and human resources analytics.

Find the visual representation and difference between: Data Lake vs Data Warehouse.

Data Lake vs Datawarehouse: Key Differences

Features	Data Lake	Data Warehouse
Purpose	Used for storing vast amounts of diverse data types for future analysis.	Optimized for large-scale analytical queries, storing historical data for reporting and analysis.
Data Type	Stores raw, unprocessed data in its native format.	Stores summarized, aggregated, and historical data.
Data Structure	Schema-on-read, allowing for flexibility in data storage.	Optimized for read-heavy operations (OLAP – Online Analytical Processing).
Users	Primarily used by data engineers, data scientists, and machine learning teams.	Mainly used by business analysts, data scientists, and decision-makers for insights and reporting.
Data Volume	Holds vast amounts of unstructured and structured data.	Handles large volumes of historical data from various sources.
Performance	Performance can vary; optimized for large data ingestion rather than query speed.	High performance for complex queries and large-scale data retrieval for analysis.
Schema Design	Uses a flexible schema design; data is often stored without a predefined schema.	Denormalized schema (e.g., star or snowflake schema) for faster query performance
Data Processing	Processes a wide variety of data types, including structured, semi-structured, and unstructured data.	Processes complex queries requiring significant data aggregation
Concurrency	Supports high concurrency for data ingestion and retrieval.	Supports a lower number of users.
Storage Cost	Typically cheaper to store vast amounts of data due to lower storage costs.	Higher storage costs due to large datasets and complex processing requirements.
Example Use Cases	Data exploration, machine learning, real-time analytics.	Business intelligence reporting, trend analysis, forecasting, decision support.
Data Source	Captures data from various sources, including social media, IoT devices, and unstructured data.	Aggregates data from multiple sources, including databases, external systems, and log files.

Data Type:
Data Lake: Stores raw, unprocessed data in its native format.
Data Warehouse: Stores summarized, aggregated, and historical data.
Purpose:
Data Lake: Used for storing vast amounts of diverse data types for future analysis.
Data Warehouse: Optimized for large-scale analytical queries and historical data analysis.
Data Structure:
Data Lake: Schema-on-read, allowing for flexibility in data storage.
Data Warehouse: Optimized for read-heavy operations (OLAP – Online Analytical Processing).
Users:
Data Lake: Primarily used by data engineers, data scientists, and machine learning teams.
Data Warehouse: Mainly used by business analysts, data scientists, and decision-makers.
Data Volume:
Data Lake: Holds vast amounts of unstructured and structured data.
Data Warehouse: Handles large volumes of historical data from multiple sources.
Performance:
Data Lake: Performance can vary; optimized for large data ingestion rather than query speed.
Data Warehouse: High performance for complex queries and large-scale data retrieval.
Schema Design:
Data Lake: Uses a flexible schema design; data is often stored without a predefined schema.
Data Warehouse: Denormalized schema (e.g., star or snowflake schema) for faster query performance.
Data Processing:
Data Lake: Processes a wide variety of data types, including structured, semi-structured, and unstructured data.
Data Warehouse: Processes complex queries requiring significant data aggregation.
Concurrency:
Data Lake: Supports high concurrency for data ingestion and retrieval.
Data Warehouse: Supports a lower number of users.
Storage Cost:
Data Lake: Typically cheaper to store vast amounts of data due to lower storage costs.
Data Warehouse: Higher storage costs due to large datasets and complex processing.
Data Source:
Data Lake: Captures data from various sources, including social media, IoT devices, and unstructured data.
Data Warehouse: Aggregates data from multiple sources, including databases, external systems, and log files.
Example Use Cases:
Data Lake: Data exploration, machine learning, real-time analytics.
Data Warehouse: Business intelligence reporting, trend analysis, forecasting.

Finding the Right Fit: data lake vs data warehouse

Is there room for both Data Lake and Data Warehouse in your data strategy? Explore the benefits of adopting a hybrid approach, seamlessly integrating the strengths of both solutions for comprehensive data management. Discover the factors to consider when choosing between Data Lake and Data Warehouse solutions. From cost considerations to scalability needs and varying data types and formats, find the perfect fit with Global Data 365 for your business’s unique requirements by contacting us now.

Simplify Your Data – Get a Free Consultation!

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Jet Analytics Data Warehouse

Global Data 365
March 26, 2021

To remain competitive, a business needs to implement a jet Data Warehouse system that can keep with future requirements. Preparing to implement an ERP system can be a challenging task. Data storage technology’s future will be characterized by speed, convenience, and efficiency. Many of Microsoft’s legacy GP, NAV, and SL customers are likely to be considering a shift to the new platform now that the company’s latest ERP software version for small and midsize businesses has been released.

Microsoft Dynamics 365 (D365 BC) is the next version of the Microsoft Dynamics NAV code base. It is expanding it to a more cloud-friendly platform and incorporating it more deeply than before with the remainder of the Microsoft stack. The process of data transfer is never an easy task, no matter which ERP system you are moving from. Some particular problems related to data transfer are there. Surprisingly, many of them can be easily handled using a data warehouse.

Although data warehouses have been built for a different reason (i.e. to store data for big data analytics), they can provide tremendous value during an ERP transfer. That is because an entire data warehouse solution can pay itself from the savings generated by the process of migration itself. Enterprises have access to an increasing amount of data from all departments of their sector. Controlling how data travels through the enterprise becomes increasingly important as a company gathers data in different formats.

Data warehouse Obstacles during Data Transfer

Data warehouse technology has not changed much. However, the rise of Big Data and an excess demand for data has uncovered technical vulnerabilities that some legacy warehouses are not equipped to manage. One of the first questions asked from a project team when it comes to data transfer is which data is going to be moved from the old system to the new one? Firstly, all the data is going to be transferred. Secondly, many businesses have collected a large amount of historical data. Exporting data, filtering it, cleaning it, and reformatting it for the new system costs time and money.

Then the challenge arises of matching transactions. Bringing the list of the history of customer payments and invoices is a separate thing from recreating the history, providing the details of the payments made to certain invoices in certain amounts. On the other hand, the cloud model distinguishes storage from computing, resulting in a new level of cost and performance efficiency. Enabling IT to:

– Pay for only what is used.
– Gain total cost/performance leverage.
– Reduce duplication of data.
– Eliminating loading of data.
– Multiple platforms can access the same data.

If the company continues to retain the old system intact, it will cost them time and money. If only a single person knows how to operate the system leaves, or if the system update conflicts with the old ERP software. It will cost your company time on support and maintenance.

Jet analytics Data Warehouse as a Solution

Most ERP system manager fails to think of the alternative; a data warehouse solution that contains data from your old ERP system. It contains all of the data you require for historical reference. With a data warehouse, there is no need to handle transactions on the old system.

In comparison to the cost of maintenance of an old ERP system vs. a data warehouse, the data warehouse comes out on top every time. It not only solves the issue of historical ERP records but also serves an ongoing function as well. It significantly reduces security risks. The cloud has changed the way companies handle and store data for the better. To satisfy your existing and future business needs, cloud computing will help you create a new modern data infrastructure. Your organization now has the opportunity to harness its data’s potential, delivering unmatched productivity and ROI. You are finally ready to turn your data to reveal the deeper insights that will help you make better business decisions and produce higher-quality results.

Data Warehouse as a Migration Tool

By creating a standard data model for your old and new ERP systems within the data warehouse. You can utilize the data warehouse as a migration tool itself. You may proactively use the data harmonization process among the two platforms to clean and normalize data from the old system and prepare it for transfer to the new system.

Since most people think of a data warehouse primarily as a staging area for reports. This is a creative solution to the issue of data migration that is rarely suggested.

ERP Migration without Reformatting Reports

The future is hard to foresee, but one certain thing is that the most productive data warehouses are those that can use their data efficiently to optimize operations, anticipate market shifts, and boost availability. Similarly, Jet Analytics is a reporting platform from Global Data 365 that deals with the entire Microsoft Dynamics products, which include Microsoft Dynamics CRM, AX (Axapta), NAV (Navision), GP (Great Plains), SL (Solomon), BC, and Microsoft Dynamics 365 Finance and Supply Chain Management product.

The relation between Jet Analytics and the various products of Microsoft Dynamics operates independently. Users can extract data from the ERP system, which is integrated inside the data warehouse to a harmonized data structure. The customer records of both Microsoft Dynamics GP and Microsoft Dynamics 365 Business Central may appear the same in the data warehouse. Invoice records from various systems will also appear similar.

If you are thinking about transferring data from one system to another, particularly from Microsoft’s legacy ERP products to D365 BC, you can save time and money by implementing these approaches. Jet Analytics data warehouse can offer the following benefits:

– You can automate the removal and transfer of data that you intend to migrate by linking the Jet Analytics product to your old system, storing them in the data warehouse for import to the new system.

– You can tackle the issue of historical data by using Jet Analytics to provide unlimited access to data that is too difficult or costly to transfer. This decreases the probability of security breaches, saves recourses, and improves efficiency.

– If you have used Jet Analytics to develop reports for your previous Microsoft Dynamics ERP system. You can continue using the reports for data with little to no change from your new Microsoft Dynamics ERP system. This saves considerable time and money on implementation.

– You will have the most sturdy BI and reporting platform on the market after the migration, which will remove any potential reporting inefficacies.

A Detailed View over Time

The Jet Analytics data warehouse approach enables you to view both old and new data together as a single whole. Jet Analytics helps businesses to run comparative reports that look back through different years. Information is structure and interpreted by the data warehouse as if it originated from a single system.

Any level of compliance is involved in most ERP system implementations. One such compliance is the necessity of a complete break from the past. This particular problem is tackled effectively by the Jet Analytics data warehouse approach.

Jet Analytics Data warehouse as a Solution

For the success of any business, the present and future warehouse management systems need to embrace the incorporation of BI software solutions and visualization of insight. You can get started with Jet Analytics whether you have upgraded to the latest versions of Microsoft’s ERP system. There are many advantages to implementing a Jet Analytics data warehouse system.

– Jet Analytics can be deployed ahead of an ERP framework update to give you a head start, reduce risk, and lighten the overall implementation workload. When you finally introduce a new ERP system, report creation on Jet Analytics will continue to pay off.

– By acquainting yourself and your staff with the data warehouse setting, you can obtain an understanding of the benefits of implementing the Jet Analytics data warehouse system.

It is time to unlock the potential of your data to drive your business ahead. To learn more about how Jet Analytics can benefit your company or learn to improvise with Jet Analytics training.

Contact us to get more information.